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Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
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2019
Cited alongside, same era.
X. Chen, L. Xie, J. Wu, and Q. Tian, “Progressive differentiable architecture search: Bridging the depth gap between search and evaluation,” in International Conference on Computer Vision , 2019
2019
Cited alongside, same era.
N. Nayman, A. Noy, T. Ridnik, I. Friedman, R. Jin, and L. Zelnik, “Xnas: Neural architecture search with expert advice,” in Advances in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Guo, Y. Zheng, M. Tan, Q. Chen, J. Chen, P. Zhao, and J. Huang, “Nat: Neural architecture transformer for accurate and compact architectures,” in Advances in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
X. Dong and Y. Yang, “One-shot neural architecture search via self-evaluated template network,” in International Conference on Computer Vision , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Closest in time.
A. Bulat, B. Martinez, and G. Tzimiropoulos, “Bats: Binary architecture search,” in European Conference on Computer Vision , 2020
2020
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2020
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2020
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2020
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X. Zheng, R. Ji, Q. Wang, Q. Ye, Z. Li, Y. Tian, and Q. Tian, “Rethinking performance estimation in neural architecture search,” in Computer Vision and Pattern Recognition , 2020
2020
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L. Zhuo, B. Zhang, H. Chen, L. Yang, C. Chen, Y. Zhu, and D. Doermann, “Cp-nas: Child-parent neural architecture search for 1-bit cnns,” in International Joint Conference on Artificial Intelligence , 2020
2020
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2020
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2020
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J. Wang, J. Wu, H. Bai, and J. Cheng, “M-nas: Meta neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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2020
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2020
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2020
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W. Hong, G. Li, W. Zhang, R. Tang, Y. Wang, Z. Li, and Y. Yu, “Dropnas: Grouped operation dropout for differentiable architecture search,” in International Joint Conference on Artificial Intelligence , 2020
2020
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2020
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Y. Li, M. Dong, Y. Wang, and C. Xu, “Neural architecture search in a proxy validation loss landscape,” in International Conference on Machine Learning , 2020
2020
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2020
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X. Wang, C. Xue, J. Yan, X. Yang, Y. Hu, and K. Sun, “Mergenas: Merge operations into one for differentiable architecture search,” in International Joint Conference on Artificial Intelligence , 2020
2020
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Y. Wang, W. Dai, C. Li, J. Zou, and H. Xiong, “Si-vdnas: Semi-implicit variational dropout for hierarchical one-shot neural architecture search,” in International Joint Conference on Artificial Intelligence , 2020
2020
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M. Zhang, H. Li, S. Pan, T. Liu, and S. Su, “One-shot neural architecture search via novelty driven sampling,” in International Joint Conference on Artificial Intelligence , 2020
2020
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H. Cai, C. Gan, and S. Han, “Once for all: Train one network and specialize it for efficient deployment,” in International Conference on Learning Representations , 2020
2020
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J. Fang, Y. Sun, Q. Zhang, Y. Li, W. Liu, and X. Wang, “Densely connected search space for more flexible neural architecture search,” in Computer Vision and Pattern Recognition , 2020
2020
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X. Chu, B. Zhang, and R. Xu, “Moga: Searching beyond mobilenetv3,” in International Conference on Acoustics, Speech and Signal Processing , 2020
2020
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J. Yu, P. Jin, H. Liu, G. Bender, P.-J. Kindermans, M. Tan, T. Huang, X. Song, R. Pang, and Q. Le, “Bignas: Scaling up neural architecture search with big single-stage models,” in European Conference on Computer Vision , 2020
2020
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X. Li, C. Lin, C. Li, M. Sun, W. Wu, J. Yan, and W. Ouyang, “Improving one-shot nas by suppressing the posterior fading,” in Computer Vision and Pattern Recognition , 2020
2020
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C. Li, J. Peng, L. Yuan, G. Wang, X. Liang, L. Lin, and X. Chang, “Block-wisely supervised neural architecture search with knowledge distillation,” in Computer Vision and Pattern Recognition , 2020
2020
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M. Kang, J. Mun, and B. Han, “Towards oracle knowledge distillation with neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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J. Mei, Y. Li, X. Lian, X. Jin, L. Yang, A. Yuille, and J. Yang, “Atomnas: Fine-grained end-to-end neural architecture search,” in International Conference on Learning Representations , 2020
2020
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2020
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2020
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X. Dai, D. Chen, M. Liu, Y. Chen, and L. Yuan, “Da-nas: Data adapted pruning for efficient neural architecture search,” in European Conference on Computer Vision , 2020
2020
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2020
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2020
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S. You, T. Huang, M. Yang, F. Wang, C. Qian, and C. Zhang, “Greedynas: Towards fast one-shot nas with greedy supernet,” in Computer Vision and Pattern Recognition , 2020
2020
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A. Wan, X. Dai, P. Zhang, Z. He, Y. Tian, S. Xie, B. Wu, M. Yu, T. Xu, K. Chen et al. , “Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 965–12 974
2020
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Y. Li, X. Jin, J. Mei, X. Lian, L. Yang, C. Xie, Q. Yu, Y. Zhou, S. Bai, and A. L. Yuille, “Neural architecture search for lightweight non-local networks,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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Y. Hu, Y. Liang, Z. Guo, R. Wan, X. Zhang, Y. Wei, Q. Gu, and J. Sun, “Angle-based search space shrinking for neural architecture search,” in European Conference on Computer Vision , 2020
2020
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R. Guo, C. Lin, C. Li, K. Tian, M. Sun, L. Sheng, and J. Yan, “Powering one-shot topological nas with stabilized share-parameter proxy,” in European Conference on Computer Vision , 2020
2020
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2020
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H. Phan, Z. Liu, D. Huynh, M. Savvides, K.-T. Cheng, and Z. Shen, “Binarizing mobilenet via evolution-based searching,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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2020
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G. Bender, H. Liu, B. Chen, G. Chu, S. Cheng, P.-J. Kindermans, and Q. V. Le, “Can weight sharing outperform random architecture search? an investigation with tunas,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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Z. Li, T. Xi, J. Deng, G. Zhang, S. Wen, and R. He, “Gp-nas: Gaussian process based neural architecture search,” in Computer Vision and Pattern Recognition , 2020
2020
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P. Liu, B. Wu, H. Ma, and M. Seok, “Memnas: Memory-efficient neural architecture search with grow-trim learning,” in Computer Vision and Pattern Recognition , 2020
2020
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L. Lyna Zhang, Y. Yang, Y. Jiang, W. Zhu, and Y. Liu, “Fast hardware-aware neural architecture search,” in Computer Vision and Pattern Recognition Workshops , 2020
2020
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2020
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2020
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A.-C. Cheng, C. H. Lin, D.-C. Juan, W. Wei, and M. Sun, “Instanas: Instance-aware neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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K. Nguyen, C. Fookes, and S. Sridharan, “Constrained design of deep iris networks,” IEEE Transactions on Image Processing , 2020
2020
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Y. Zhou, X. Sun, C. Luo, Z.-J. Zha, and W. Zeng, “Posterior-guided neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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J. Jiang, F. Han, Q. Ling, J. Wang, T. Li, and H. Han, “Efficient network architecture search via multiobjective particle swarm optimization based on decomposition,” Neural Networks , vol. 123, pp. 305–316, 2020
2020
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2020
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2020
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M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Computer Vision and Pattern Recognition , 2020
2020
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L. Yao, H. Xu, W. Zhang, X. Liang, and Z. Li, “Sm-nas: Structural-to-modular neural architecture search for object detection,” in AAAI Conference on Artificial Intelligence , 2020
2020
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J. Fang, Y. Sun, K. Peng, Q. Zhang, Y. Li, W. Liu, and X. Wang, “Fast neural network adaptation via parameter remapping and architecture search,” in International Conference on Learning Representations , 2020
2020
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J. Guo, K. Han, Y. Wang, C. Zhang, Z. Yang, H. Wu, X. Chen, and C. Xu, “Hit-detector: Hierarchical trinity architecture search for object detection,” in Computer Vision and Pattern Recognition , 2020
2020
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C. Jiang, S. Wang, X. Liang, H. Xu, and N. Xiao, “Elixirnet: Relation-aware network architecture adaptation for medical lesion detection,” in AAAI , 2020, pp. 11 093–11 100
2020
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2020
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C. Jiang, H. Xu, W. Zhang, X. Liang, and Z. Li, “Sp-nas: Serial-to-parallel backbone search for object detection,” in Computer Vision and Pattern Recognition , 2020
2020
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B. Chen, G. Ghiasi, H. Liu, T.-Y. Lin, D. Kalenichenko, H. Adam, and Q. V. Le, “Mnasfpn: Learning latency-aware pyramid architecture for object detection on mobile devices,” in Computer Vision and Pattern Recognition , 2020
2020
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——, “Architecture search of dynamic cells for semantic video segmentation,” in Winter Conference on Applications of Computer Vision , 2020
2020
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V. Nekrasov, C. Shen, and I. Reid, “Template-based automatic search of compact semantic segmentation architectures,” in Winter Conference on Applications of Computer Vision , 2020
2020
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P. Lin, P. Sun, G. Cheng, S. Xie, X. Li, and J. Shi, “Graph-guided architecture search for real-time semantic segmentation,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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C. Gao, Y. Chen, S. Liu, Z. Tan, and S. Yan, “Adversarialnas: Adversarial neural architecture search for gans,” in Computer Vision and Pattern Recognition , 2020
2020
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M. Li, J. Lin, Y. Ding, Z. Liu, J.-Y. Zhu, and S. Han, “Gan compression: Efficient architectures for interactive conditional gans,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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2020
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2020
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M. Baldeon-Calisto and S. K. Lai-Yuen, “Adaresu-net: Multiobjective adaptive convolutional neural network for medical image segmentation,” Neurocomputing , vol. 392, pp. 325–340, 2020
2020
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Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A. L. Yuille, and D. Xu, “C2fnas: Coarse-to-fine neural architecture search for 3d medical image segmentation,” in Computer Vision and Pattern Recognition , 2020
2020
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D. Guo, D. Jin, Z. Zhu, T.-Y. Ho, A. P. Harrison, C.-H. Chao, J. Xiao, and L. Lu, “Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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M. Guo, Y. Yang, R. Xu, Z. Liu, and D. Lin, “When nas meets robustness: In search of robust architectures against adversarial attacks,” in Computer Vision and Pattern Recognition , 2020
2020
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D. Song, C. Xu, X. Jia, Y. Chen, C. Xu, and Y. Wang, “Efficient residual dense block search for image super-resolution,” in AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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M. Mozejko, T. Latkowski, L. Treszczotko, M. Szafraniuk, and K. Trojanowski, “Superkernel neural architecture search for image denoising,” in Computer Vision and Pattern Recognition Workshops , 2020
2020
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R. Li, R. T. Tan, and L.-F. Cheong, “All in one bad weather removal using architectural search,” in Computer Vision and Pattern Recognition , 2020
2020
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H. Zhang, Y. Li, H. Chen, and C. Shen, “Memory-efficient hierarchical neural architecture search for image denoising,” in Computer Vision and Pattern Recognition , 2020
2020
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R. Lee, Ł. Dudziak, M. Abdelfattah, S. I. Venieris, H. Kim, H. Wen, and N. D. Lane, “Journey towards tiny perceptual super-resolution,” in European Conference on Computer Vision , 2020
2020
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M. S. Ryoo, A. Piergiovanni, M. Tan, and A. Angelova, “Assemblenet: Searching for multi-stream neural connectivity in video architectures,” in International Conference on Learning Representations , 2020
2020
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Y. Yu, Y. Li, S. Che, N. K. Jha, and W. Zhang, “Software-defined design space exploration for an efficient dnn accelerator architecture,” IEEE Transactions on Computers , 2020
2020
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Y. Gao, H. Bai, Z. Jie, J. Ma, K. Jia, and W. Liu, “Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning,” in Computer Vision and Pattern Recognition , 2020
2020
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Y. Wang, Y. Yang, Y. Chen, J. Bai, C. Zhang, G. Su, X. Kou, Y. Tong, M. Yang, and L. Zhou, “Textnas: A neural architecture search space tailored for text representation,” in AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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2020
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2020
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2020
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T. Li, J. Zhang, K. Bao, Y. Liang, Y. Li, and Y. Zheng, “Autost: Efficient neural architecture search for spatio-temporal prediction,” in Knowledge Discovery & Data Mining , 2020
2020
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2020
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2020
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2020
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J. An, H. Xiong, J. Huan, and J. Luo, “Ultrafast photorealistic style transfer via neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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S. Bianco, M. Buzzelli, G. Ciocca, and R. Schettini, “Neural architecture search for image saliency fusion,” Information Fusion , vol. 57, pp. 89–101, 2020
2020
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2020
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2020
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2020
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X. Du, T.-Y. Lin, P. Jin, G. Ghiasi, M. Tan, Y. Cui, Q. V. Le, and X. Song, “Spinenet: Learning scale-permuted backbone for recognition and localization,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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H. Chen, B. Z. Li’an Zhuo, X. Zheng, J. Liu, D. Doermann, and R. Ji, “Binarized neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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X. Ning, Y. Zheng, T. Zhao, Y. Wang, and H. Yang, “A generic graph-based neural architecture encoding scheme for predictor-based nas,” in European Conference on Computer Vision , 2020
2020
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Y. Tang, Y. Wang, Y. Xu, H. Chen, B. Shi, C. Xu, C. Xu, Q. Tian, and C. Xu, “A semi-supervised assessor of neural architectures,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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2020
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2020
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2020
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2020
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2020
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2020
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X. Dong and Y. Yang, “Nas-bench-201: Extending the scope of reproducible neural architecture search,” in International Conference on Learning Representations , 2020
2020
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2020
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Y. Xu, Y. Wang, K. Han, S. Jui, C. Xu, Q. Tian, and C. Xu, “Renas: Relativistic evaluation of neural architecture search,” in AAAI Conference on Artificial Intelligence , 2020
2020
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I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollár, “Designing network design spaces,” in Computer Vision and Pattern Recognition , 2020
2020
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A. Yang, P. M. Esperança, and F. M. Carlucci, “Nas evaluation is frustratingly hard,” in International Conference on Learning Representations , 2020
2020
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K. Yu, C. Sciuto, M. Jaggi, C. Musat, and M. Salzmann, “Evaluating the search phase of neural architecture search,” in International Conference on Learning Representations , 2020
2020
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2020
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2020
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2020
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2020
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2020
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K. Kandasamy, K. R. Vysyaraju, W. Neiswanger, B. Paria, C. R. Collins, J. Schneider, B. Poczos, and E. P. Xing, “Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly,” Journal of Machine Learning Research , vol. 21, no. 81, pp. 1–27, 2020
2020
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2020
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2020
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X. Chen, Y. Duan, Z. Chen, H. Xu, Z. Chen, X. Liang, T. Zhang, and Z. Li, “Catch: Context-based meta reinforcement learning for transferrable architecture search,” in European Conference on Computer Vision , 2020
2020
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A. Zela, J. Siems, and F. Hutter, “Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search,” in International Conference on Learning Representations , 2020
2020
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M. G. d. Nascimento, T. W. Costain, and V. A. Prisacariu, “Finding non-uniform quantization schemes usingmulti-task gaussian processes,” in European Conference on Machine Learning , 2020
2020
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H. Yu, Q. Han, J. Li, J. Shi, G. Cheng, and B. Fan, “Search what you want: Barrier panelty nas for mixed precision quantization,” in European Conference on Computer Vision , 2020
2020
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X. Lu, H. Huang, W. Dong, X. Li, and G. Shi, “Beyond network pruning: a joint search-and-training approach,” in International Joint Conference on Artificial Intelligence , 2020
2020
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T. Wang, K. Wang, H. Cai, J. Lin, Z. Liu, H. Wang, Y. Lin, and S. Han, “Apq: Joint search for network architecture, pruning and quantization policy,” in Computer Vision and Pattern Recognition , 2020
2020
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Y. Wang, X. Zhang, L. Xie, J. Zhou, H. Su, B. Zhang, and X. Hu, “Pruning from scratch,” in AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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Z. Chen, J. Niu, L. Xie, X. Liu, L. Wei, and Q. Tian, “Network adjustment: Channel search guided by flops utilization ratio,” in Computer Vision and Pattern Recognition , 2020
2020
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Z. Cai and N. Vasconcelos, “Rethinking differentiable search for mixed-precision neural networks,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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2020
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W. Jiang, Q. Lou, Z. Yan, L. Yang, J. Hu, X. S. Hu, and Y. Shi, “Device-circuit-architecture co-exploration for computing-in-memory neural accelerators,” IEEE Transactions on Computers , 2020
2020
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W. Jiang, L. Yang, E. H.-M. Sha, Q. Zhuge, S. Gu, S. Dasgupta, Y. Shi, and J. Hu, “Hardware/software co-exploration of neural architectures,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2020
2020
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2020
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2020
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Y. Li, L. Song, Y. Chen, Z. Li, X. Zhang, X. Wang, and J. Sun, “Learning dynamic routing for semantic segmentation,” in Computer Vision and Pattern Recognition , 2020
2020
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T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter, “Meta-learning of neural architectures for few-shot learning,” in Computer Vision and Pattern Recognition , 2020
2020
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Z. Li and S. Arora, “An exponential learning rate schedule for deep learning,” in International Conference on Learning Representations , 2020
2020
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2020
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T. Lancewicki and S. Kopru, “Automatic and simultaneous adjustment of learning rate and momentum for stochastic gradient-based optimization methods,” in International Conference on Acoustics, Speech and Signal Processing , 2020
2020
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2020
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2020
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E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in Computer Vision and Pattern Recognition Workshops , 2020
2020
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X. Zhang, Q. Wang, J. Zhang, and Z. Zhong, “Adversarial autoaugment,” in International Conference on Learning Representations , 2020
2020
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C. Xie, M. Tan, B. Gong, J. Wang, A. L. Yuille, and Q. V. Le, “Adversarial examples improve image recognition,” in Computer Vision and Pattern Recognition , 2020
2020
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L. Wei, A. Xiao, L. Xie, X. Chen, X. Zhang, and Q. Tian, “Circumventing outliers of autoaugment with knowledge distillation,” in European Conference on Computer Vision , 2020
2020
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2020
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J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang et al. , “Deep high-resolution representation learning for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
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2020
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Y. Liu, X. Jia, M. Tan, R. Vemulapalli, Y. Zhu, B. Green, and X. Wang, “Search to distill: Pearls are everywhere but not the eyes,” in Computer Vision and Pattern Recognition , 2020
2020
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2020
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